Update README.md

README.md

@@ -45,16 +45,16 @@ This repo contains GGUF format model files for [tanamettpk's TC Instruct DPO](ht
 
 ### About GGUF
 
-GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.
+GGUF is a new format introduced by the llama.cpp team on August 21st, 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenization, and support for special tokens. It also supports metadata and is designed to be extensible.
 
-Here is an incomplate list of clients and libraries that are known to support GGUF:
+Here is an incomplete list of clients and libraries that are known to support GGUF:
 
 * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
 * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
-* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
+* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for storytelling.
 * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
 * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
-* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
+* [Faraday.dev](https://faraday.dev/), an attractive and easy-to-use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
 * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
 * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
 * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
@@ -73,15 +73,15 @@ Here is an incomplate list of clients and libraries that are known to support GG
 
 These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
 
-They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
+They are also compatible with many third-party UIs and libraries - please see the list at the top of this README.
 
-## Explanation of quantisation methods
+## Explanation of quantization methods
 <details>
   <summary>Click to see details</summary>
 
 The new methods available are:
 * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
-* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
+* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This ends up using 3.4375 bpw.
 * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
 * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
 * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
@@ -109,7 +109,7 @@ Refer to the Provided Files table below to see what files use which methods, and
 
 ## How to download GGUF files
 
-**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
+**Note for manual downloaders:** You rarely want to clone the entire repo! Multiple different quantization formats are provided, and most users only want to pick and download a single file.
 
 The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
 
@@ -119,7 +119,7 @@ The following clients/libraries will automatically download models for you, prov
 
 ### In `text-generation-webui`
 
-Under Download Model, you can enter the model repo: TheBloke/Llama-2-13B-GGUF and below it, a specific filename to download, such as: llama-2-13b.q4_K_M.gguf.
+Under Download Model, you can enter the model repo: TheBloke/Llama-2-13B-GGUF, and below it, a specific filename to download, such as llama-2-13b.q4_K_M.gguf.
 
 Then click Download.
 
@@ -206,10 +206,10 @@ CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
 # Or with Metal GPU acceleration for macOS systems only
 CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
 
-# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for Nvidia CUDA:
+# In Windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for Nvidia CUDA:
 $env:CMAKE_ARGS = "-DLLAMA_CUDA=on"
 pip install llama_cpp_python --verbose
-# If BLAS = 0 try installing with these command instead (Windows + CUDA)
+# If BLAS = 0 try installing with these commands instead (Windows + CUDA)
 set CMAKE_ARGS="-DLLAMA_CUDA=on"
 set FORCE_CMAKE=1
 $env:CMAKE_ARGS = "-DLLAMA_CUDA=on"
@@ -275,7 +275,7 @@ print(response)
 
 ## How to use with LangChain
 
-Here's guides on using llama-cpp-python or ctransformers with LangChain:
+Here are guides on using llama-cpp-python or ctransformers with LangChain:
 
 * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
 * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)